Uneven Lighting Research Articles

Enhancing Underwater Video from Consecutive Frames While Preserving Temporal Consistency

Current methods for underwater image enhancement primarily focus on single-frame processing. While these approaches achieve impressive results for static images, they often fail to maintain temporal coherence across frames in underwater videos, which leads to temporal artifacts and frame flickering. Furthermore, existing enhancement methods struggle to accurately capture features in underwater scenes. This makes it difficult to handle challenges such as uneven lighting and edge blurring in complex underwater environments. To address these issues, this paper presents a dual-branch underwater video enhancement network. The network synthesizes short-range video sequences by learning and inferring optical flow from individual frames. It effectively enhances temporal consistency across video frames through predicted optical flow information, thereby mitigating temporal instability within frame sequences. In addition, to address the limitations of traditional U-Net models in handling complex multiscale feature fusion, this study proposes a novel underwater feature fusion module. By applying both max pooling and average pooling, this module separately extracts local and global features. It utilizes an attention mechanism to adaptively adjust the weights of different regions in the feature map, thereby effectively enhancing key regions within underwater video frames. Experimental results indicate that when compared with the existing underwater image enhancement baseline method and the consistency enhancement baseline method, the proposed model improves the consistency index by 30% and shows a marginal decrease of only 0.6% in enhancement quality index, demonstrating its superiority in underwater video enhancement tasks.

Defect And Damage Characterization Of Additively Manufactured Titanium Alloy Ti-5553 Using Traditional Computed Tomography Volume Segmentation And Machine Learning Algorithms

The mechanical response of a component is affected by defects, such as porosity, arising from the laser powder bed fusion (LPBF) fabrication process. Thus, it is important to develop accurate and efficient inspection methods for identifying porosity. In this work, porosity identified in an X-ray computed tomography (XCT) volume of a Ti-5553 coupon was compared to pores identified in a serial sectioned volume that represented the ground truth. The porosity of the XCT scan was identified using contrast-based, ISO-based, and machine learning (ML) methods for segmentation. Large inherent porosity was easy to identify, but the ISO thresholding still struggled due to the intensity gradient resulting from both the beam hardening in XCT and the uneven lighting of the serial sectioning panels. The results show that ML-based methods were better suited for identifying small pores and reducing the amount of false positives. Additionally, high strain-rate impact testing was done on some of the XCT samples as well as post-mortem XCT inspection, and the same suite of segmentation and quantification tools were used to identify the large spallation cavities. The comparison of porosity pre- and post-mortem provides insight on the influence of the LPBF porosity on the formation of spall cavities.

FUZZY REGION MERGING WITH HIERARCHICAL CLUSTERING TO FIND OPTIMAL INITIALIZATION OF FUZZY REGION IN IMAGE SEGMENTATION

One of the most important goals in image segmentation is the process of separating the object parts from the image background. Image segmentation is also a fundamental stage in the development of other image applications such as object recognition, target tracking, computer vision, and biomedical image processing. Interactive image segmentation methods with additional user interaction are still popular in research. Interactive image segmentation aims to provide additional information through simple interactions, especially in images with complex objects. Interactive image segmentation with region merging processes has drawbacks, one of which is suboptimal region splitting due to soft color shades, blurred contours, and uneven lighting, referred to in this study as ambiguous regions. However, in the fuzzy region initialization stage after obtaining values from the marker process, there is a possibility of missing or suboptimal determination of fuzzy regions. This is because it only takes the highest gray level value for the background marker and the lowest gray level value for the object marker. In this study, fuzzy region merging using hierarchical clustering is proposed to find optimal initialization for fuzzy regions in image segmentation. Based on the experimental results, the proposed method can achieve optimal segmentation with an average misclassification error value of 2.62% for Natural Images and 9.33% for Dental Images.

Visible light visual indoor positioning system for based on residual convolutional networks and image restoration

Abstract Visible light positioning technology, with its advantages of low cost, strong anti-interference, and high precision, is widely researched and applied in various different scenarios. In this paper, for the complexity of indoor environments, considering the problem of occlusion by various obstacles that may exist in indoor spaces, which may lead to incomplete imaging of CMOS image sensors, a maximum gray value-based occlusion recovery and decoding scheme is proposed. This scheme effectively solves the problem of visible light transmission channel being blocked and accomplishes LED-ID decoding. In addition, the overflow effect due to uneven light irradiation gathered in each pixel row affects the accuracy of decoding LED-ID, which in turn leads to poor positioning accuracy. In this paper, we propose to use an adaptive gamma correction method to eliminate the influence of the overflow effect and to improve the accuracy of decoding. In order to improve the positioning accuracy, a visible light positioning algorithm based on residual convolutional network (VisiResNet) is proposed to achieve high accuracy positioning. The experimental results show that the average positioning error is 9.7 cm in a space of 9m× 3m× 3m, and a decoding accuracy of 90% within 1.4m is achieved in the face of different occlusion situations. The system can achieve centimeter-level positioning accuracy and meet the indoor positioning requirements.

MSDCE: Light-Enhancement Curve-based Algorithm for Improving Visual Quality of Images with Uneven Lightening

Enhancing low-light images under uneven illumination remains a challenging problem in computer vision. This study proposes an enhanced version of the Zero-Reference Deep Curve Estimation (Zero-DCE) model, named MS-DCE (Multi-Scale Deep Curve Estimation). The proposed model incorporates comprehensive architectural modifications and refined loss functions to improve performance. Specifically, multi-scale convolution is introduced to capture contextual information at varying scales, depth-wise separable convolutions are employed to reduce model parameters and computational cost, and traditional up-sampling is replaced with PixelShuffle to improve image resolution. Additionally, the loss functions are refined to mitigate overexposure while preserving natural colour consistency, thereby enhancing visual quality, particularly in regions with uneven lighting. Experimental results on the Part 2 subset of the SICE dataset demonstrate substantial improvements in image quality, with a 2% increase in PSNR and a 4% improvement in perceptual quality. These modifications not only enhance low-light image recovery but also provide a more efficient solution for handling complex illumination conditions.

Mine underground object detection algorithm based on TTFNet and anchor-free

Abstract To solve the problem of poor object detection effect caused by uneven light and high noise in underground mines, this study proposes a TTFNet (training-time-friendly network)-based object detection algorithm for underground mines. First, CenterNet and TTFNet algorithms are introduced, then pooling is introduced into CSPNet basic structure to design a lightweight feature extraction network, at the same time optimizing the feature fusion way in the original algorithm, optimizing residual shrinkage network structure, and introducing it into object detection task. Experiments were conducted on the established underground data set. The results show that compared with the original algorithm, our proposed algorithm can still maintain similar accuracy while significantly reducing model parameters; compared with other anchor-based detection algorithms, it has achieved similar overall performance.

EC-WAMI: Event Camera-Based Pose Optimization in Remote Sensing and Wide-Area Motion Imagery.

In this paper, we present EC-WAMI, the first successful application of neuromorphic event cameras (ECs) for Wide-Area Motion Imagery (WAMI) and Remote Sensing (RS), showcasing their potential for advancing Structure-from-Motion (SfM) and 3D reconstruction across diverse imaging scenarios. ECs, which detect asynchronous pixel-level brightness changes, offer key advantages over traditional frame-based sensors such as high temporal resolution, low power consumption, and resilience to dynamic lighting. These capabilities allow ECs to overcome challenges such as glare, uneven lighting, and low-light conditions that are common in aerial imaging and remote sensing, while also extending UAV flight endurance. To evaluate the effectiveness of ECs in WAMI, we simulate event data from RGB WAMI imagery and integrate them into SfM pipelines for camera pose optimization and 3D point cloud generation. Using two state-of-the-art SfM methods, namely, COLMAP and Bundle Adjustment for Sequential Imagery (BA4S), we show that although ECs do not capture scene content like traditional cameras, their spike-based events, which only measure illumination changes, allow for accurate camera pose recovery in WAMI scenarios even in low-framerate(5 fps) simulations. Our results indicate that while BA4S and COLMAP provide comparable accuracy, BA4S significantly outperforms COLMAP in terms of speed. Moreover, we evaluate different feature extraction methods, showing that the deep learning-based LIGHTGLUE descriptor consistently outperforms traditional handcrafted descriptors by providing improved reliability and accuracy of event-based SfM. These results highlight the broader potential of ECs in remote sensing, aerial imaging, and 3D reconstruction beyond conventional WAMI applications. Our dataset will be made available for public use.

FPGA-based low-light image enhancement using Retinex algorithm and coarse-grained reconfigurable architecture

Advancements in digital imaging and video processing are often challenged by low-light environments, leading to degraded visual quality. This affects critical sectors such as medical imaging, aerospace, and underwater exploration, where uneven lighting can compromise safety and clarity. To enhance image quality in low-light conditions using a computationally efficient system. This paper introduces an FPGA-based system utilizing the Retinex algorithm for low-light image enhancement, implemented on a Coarse-Grained Reconfigurable Architecture (CGRA). The system is designed using Verilog HDL on a Xilinx FPGA, prioritizing hardware optimization to achieve high-quality outputs with minimal latency. The system achieves a processing rate of 60 frames per second (fps) for images with a resolution of 720 × 576. Quantitative evaluations show a Peak Signal-to-Noise Ratio (PSNR) improvement to 43.18 dB, a Structural Similarity Index (SSIM) of 0.92, and a Mean Squared Error (MSE) reduction, demonstrating significant enhancements in image quality. The design also achieves a low power consumption of 0.186 W and efficient resource utilization, with only 2.2% of Slice LUTs and Slice Registers used. The FPGA-based system demonstrates significant improvements in image quality with high computational efficiency, proving beneficial for critical applications in various sectors.

Optimized binarization algorithm-based method for the image recognition and characterization of explosion damage in rock masses

The quantitative analysis of rock mass damage is crucial in fields such as engineering geology, disaster prevention, mining, geotechnical engineering, and structural engineering. With the advancement and application of noncontact measurement technologies and fractal theory, image-based damage identification methods are gaining increasing importance. This paper presents an optimized binarization algorithm for identifying and characterizing damage zones in granite explosion images. The method involves filtering, mathematical morphology operations, and connectivity recognition to effectively remove background noise while preserving clear boundaries of the damaged areas. It accurately captures the explosion damage in granite, both in terms of damage morphology and characteristic parameters. Additionally, the coefficient of agreement (COA) is introduced to quantitatively assess the accuracy of different methods in identifying damaged areas. The experimental results show that, compared with commonly used methods such as Otsu's method, Bernsen's algorithm, Niblack's algorithm, Sauvola's algorithm, and the K-means image clustering algorithm, the proposed method performs better in terms of identification accuracy and parameter agreement, achieving COA values near 1 across diverse experimental environments. Furthermore, the proposed method excels in handling uneven lighting, mitigating interference from rock surface textures and explosion carbonization zones, and demonstrates significant robustness in complex scenarios. The findings of this paper provide insights into the integration of engineering geology and computer vision technology. They offer valuable references for damage identification in excavation damage zones (EDZs), geological disaster evaluation, and structural damage warning systems.

Robot Localization Method Based on Multi-Sensor Fusion in Low-Light Environment

When robots perform localization in indoor low-light environments, factors such as weak and uneven lighting can degrade image quality. This degradation results in a reduced number of feature extractions by the visual odometry front end and may even cause tracking loss, thereby impacting the algorithm’s positioning accuracy. To enhance the localization accuracy of mobile robots in indoor low-light environments, this paper proposes a visual inertial odometry method (L-MSCKF) based on the multi-state constraint Kalman filter. Addressing the challenges of low-light conditions, we integrated Inertial Measurement Unit (IMU) data with stereo vision odometry. The algorithm includes an image enhancement module and a gyroscope zero-bias correction mechanism to facilitate feature matching in stereo vision odometry. We conducted tests on the EuRoC dataset and compared our method with other similar algorithms, thereby validating the effectiveness and accuracy of L-MSCKF.

Text Detection on Industrial Barrel Label with Convolutional Attention and Dual‐Branch Feature Network

Industrial barrel labels generally have low visual contrast, uneven lighting, and cluttered background, making it challenging to accurately locate text regions. This paper proposes a text detection network to solve the inaccurate localization problem based on DBNet. First, a convolutional attention mechanism is applied to the feature extraction network to get more valuable text feature maps. Then, a dual‐branch convolutional feature module is proposed in the feature pyramid to enrich contextual information. Besides, during the probability map generation stage, using a feature remodeling enhancement module to further distinguish text and text boundaries. This paper designs comparative experiments on ILTD, ICDAR2015 and MSRA‐TD500 datasets, achieve F‐measure of 92.3%, 86.0% and 84.1%, which are 2.2%, 2.3%, and 1.9% higher than DBNet, respectively. They demonstrate that our proposed method exhibits competitive performance and strong robustness. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Research on Temporal–Spatial Partition Control Strategies for Luminous and Thermal Environment in High Space of Gymnasiums

The lighting design of large-space buildings in gymnasiums can impact the indoor luminous and thermal environment, resulting in an uneven light and thermal distribution. This paper investigates the luminous and thermal environment control strategies for high spaces in gymnasiums, by simulating the luminous and thermal environment under different lighting forms and establishing a comprehensive evaluation model. The results show that the weights of the indoor luminous environment, thermal environment, and comprehensive energy consumption change with season and time under different lighting forms, which provides a basis for developing a temporal–spatial partition control strategy. The temporal–spatial partition control strategy is proposed for summer and winter, including the shading angle control under the lighting forms of south-facing side windows, west-facing side windows, and top skylights. Under summer conditions, the south-facing side windows have no shading from 8:00 to 10:00 and 14:00 to 16:00, and the shading angle is 0° from 10:00 to 14:00; the west-facing side windows have no shading from 8:00 to 14:00, the shading angle is 0° from 14:00 to 15:00, and the shading angle is 15° from 15:00 to 16:00; and the top skylight has a shade angle of 15° from 8:00 to 9:00, 30° from 9:00 to 11:00, 45° from 11:00 to 13:00, and no shade from 13:00 to 16:00. Under winter conditions, the south-facing side windows have no shading all day; the west-facing side windows have no shading from 8:00 to 14:00, and the shading angle is 30° from 14:00 to 16:00; and the top skylight has no shading from 8:00 to 13:00, a shading angle of 45° from 13:00 to 15:00, and a shading angle of 75° from 15:00 to 16:00. This paper provides a set of scientific and reasonable luminous and thermal environment regulation strategies for large-space buildings, which can help optimize the building energy consumption and improve the indoor environment quality.

Image shadow removal via multi-scale deep Retinex decomposition

In recent years, deep learning has emerged as an important tool for image shadow removal. However, existing methods often prioritize shadow detection and, in doing so, they oversimplify the lighting conditions of shadow regions. Furthermore, these methods neglect cues from the overall image lighting when re-lighting shadow areas, thereby failing to ensure global lighting consistency. To address these challenges in images captured under complex lighting conditions, this paper introduces a multi-scale network built on a Retinex decomposition model. The proposed approach effectively senses shadows with uneven lighting and re-light them, achieving greater consistency along shadow boundaries. Furthermore, for the design of network, we introduce several techniques for boosting shadow removal performance, including a shadow-aware channel attention module, local discriminative and Retinex decomposition loss functions, and a multi-scale mechanism for guiding Retinex decomposition by concurrently capturing both fine-grained details and large-scale contextual information. Experimental results demonstrate the superiority of our proposed method over existing solutions, particularly for images taken under complex lighting conditions.

Growing Tomato Seedlings Suitable for Mechanical Grafting under Regulated Light Regime

Abstract: The uniformity of growth and mechanical properties of grafted seedlings affect the quality of mechanical grafting operations. The growth uniformity of grafted seedlings in a greenhouse will be poor due to the uneven and unstable light and temperature conditions. Plant factories can cultivate grafted seedlings in the most suitable environment by regulating environmental parameters such as light and temperature. The aim of this study was to investigate the impact of the light conditions on tomato seedlings in plant factory and to develop an optimal cultivation light formula. The effects of light intensity (50, 100, 150, 200, and 250 μmol m−2 s−2) and photoperiod (10, 12, 14, 16, and 18 h a day(h/d)) on the morphological and mechanical properties of tomato seedlings were experimentally investigated. Orthogonal experiments were conducted involving light quality (R:B = 75:25, R:B = 50:50, and R:B = 25:75), light intensity (150 μmol m−2 s−2, 200 μmol m−2 s−2, and 250 μmol m−2 s−2), and photoperiod (14, 16, and 18 h/d) as independent variables to determine the optimal combination. Finally, a comparative grafting experiment was conducted between the seedlings cultivated using the optimal light formula and commercially available seedlings. The result showed that increasing light intensity inhibited hypocotyl length and promoted seedling stem growth, and excessive light intensity decreased seedling mechanical properties. The optimal light intensity for rootstocks is 200 μmol m−2 s−2, and the optimal light intensity for scions is 250 μmol m−2 s−2. Shortening the photoperiod would promote hypocotyl growth and inhibit seedling stem elongation. Different photoperiods had a significant impact on the mechanical properties of tomato seedlings. The most suitable photoperiod for rootstocks was 18 h/d and for scions was 16 h/d. The most suitable light formula was R:B = 50:50, 250 μmol m−2 s−2, 18 h/d. By analyzing the experimental results, the mechanical properties of seedlings grown by the regulated light environment were better than those of commercially available seedlings, and the success rate of mechanical grafting was 7% higher. Overall, in plant factories compared to commercially available tomato seedlings, tomato seedlings cultivated by the regulated light environment were more suitable for mechanical grafting. This research result provides theoretical support for subsequent research on grafting machinery.

A Multispectral Blood Smear Background Images Reconstruction for Malaria Unstained Images Normalization

ABSTRACTMultispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its overall outcomes. To overcome this limitation, it is crucial to perform normalization on the acquired multispectral images as a preprocessing step for malaria parasite detection. In this study, we propose a novel method for achieving this normalization, aiming to improve the accuracy and reliability of the diagnostic process. This method is based on estimating the Bright reference image, which captures the luminosity, and the contrast variability function from the background region of the image. This is achieved through two distinct resampling methodologies, namely Gaussian random field simulation by variogram analysis and Bootstrap resampling. A method for handling the intensity saturation issue of certain pixels is also proposed, which involves outlier imputation. Both of these proposed approaches for image normalization are demonstrated to outperform existing methods for multispectral and multimodal unstained blood smear images, as measured by the Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Zero mean Sum of Absolute Differences (ZSAD), Peak Signal to Noise Ratio (PSNR), and Absolute Mean Brightness Error (AMBE). These methods not only improve the image contrast but also preserve its spectral footprint and natural appearance more accurately. The normalization technique employing Bootstrap resampling significantly reduces the acquisition time for multimodal and multispectral images by 66%. Moreover, the processing time for Bootstrap resampling is less than 4% of the processing time required for Gaussian random field simulation.

CP-RDM: a new object detection algorithm for casting and pouring robots

AbstractAutomating the casting sector heavily relies on pivotal technology for object detection in pouring robots. A sophisticated algorithm designed to identify and locate target pouring holes in intricate casting workshops is crucial for advancing the intelligence of the casting process. However, the workshop environment for pouring is generally challenging, with uneven lighting, varying sizes of pouring holes, and significant occlusion in the target area, all impacting the accuracy of target detection tasks. To overcome these challenges, this paper proposes enhancing the YOLOv8s algorithm for object detection in pouring robots. Firstly, to address the issue of different scales in pouring holes, a Multi-Scale Residual Channel and Spatial Information Fusion Module (MRCS) is designed to aggregate channel and spatial information, thereby enhancing the feature extraction capability of the model. The proposed enhancement is validated on the Pascal VOC dataset. Secondly, a SimAM attention mechanism is added at the end of the backbone network to focus the object detection network more on the positional region of the pouring hole. Importantly, this addition does not introduce extra parameters or computational burden to the model. Finally, in the detection part of the model, the detection head from the RT-DETR model is introduced. This combination of real-time detection capability from YOLO and deep feature extraction capability from RT-DETR enhances the detection accuracy of the model while ensuring real-time performance. Experimental results on the updated pouring hole dataset reveal that, with only a slight increase in parameters, the proposed model achieves a 2.5% and 3.5% improvement in mAP@0.5 and F1-Score, respectively, compared to the baseline algorithm YOLOv8s. Precision (P) is enhanced by 1.8%, recall (R) by 3.5%, and PFS reaches 110, meeting the requirements for real-time pouring in pouring robots.

Tracking and automatic behavioral analysis of group-housed pigs based on YOLOX+BoT-SORT-slim

Automatic analysis of pig behavior is crucial for assessing their health and welfare status in group-housed pig farms. Currently, computer vision technology has been widely applied to recognize and track pig behavior. However, these applications lack the capability for automatic behavior analysis. Additionally, execution speed is crucial for long-term tracking, as the tracking target can easily be lost due to the complex background of pig farms, uneven lighting, and the similar appearance of pigs. To tackle these challenges, we propose a lightweight multi-object tracking (MOT) and behavioral analysis method: YOLOX+BoT-SORT-Slim. Firstly, the YOLOX detector detects pig targets and recognizes the pigs’ four behaviors including “stand”, “lie”, “eat”, and “other”. Secondly, the BoT-SORT-Slim algorithm tracks the detected pigs and their behaviors at high speed. Finally, we devise a behavior automatic analysis algorithm that integrates the behavioral information and the tracking results to complete pigs’ behavior analysis. To evaluate the proposed method, we conducted experiments on 1-minute, 10-minute, and 1-hour datasets. The experimental results show that on the 1-minute public and private test sets, the proposed method achieves 6.4 and 3.6 times the frame rate (FPS) of the original algorithm, with 68.05 FPS and 71.27 FPS, respectively. It also achieves a higher-order tracking accuracy (HOTA) of 84.7 % and 78.7 %, multi-object tracking accuracy (MOTA) of 98.3 % and 97.0 %, and an identification F1 score (IDF1) of 98.4 % and 93.4 %. The proposed method obtains the best results on all test sets compared to five other MOT algorithms including DeepSORT, StrongSORT, ByteTrack, OC-SORT, and BoT-SORT. In the 10-minute and 1-hour test sets, the proposed method achieves 70.28 and 72.24, 63.3 % and 76.4 %, 94.1 % and 99.6 %, and 72.5 % and 90.2 % for FPS, HOTA, MOTA, and IDF1, respectively. In terms of behavior analysis, three types of pigs’ behavior analysis are plotted for each test video to assess their health status. The experimental results indicate that the proposed method performs excellently in behavior analysis and tracking, which can reliably monitor and manage pig behavior in real time, providing automated management technical support.

A Distorted Light Field Image Correction Method Based on Improved Hough Transform

Introduction: In using a camera to take photos, due to environmental limitations, uneven lighting can cause uneven distribution of the image light field, resulting in distortion of the image background and target, blurring of details, and distorted light field images. Method: In view of this, research is conducted on the correction of distorted light field images based on the Hough transform. First, the improved Hough transform is utilized to locate the four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets the correction result of the distorted light field image. Result: The research results indicate that in the practical application of the distorted light field image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one. Conclusion: In comparative experiments, the research algorithm outperforms the other three algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of 54.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction results.

Research on Detection Method of Chaotian Pepper in Complex Field Environments Based on YOLOv8.

The intelligent detection of chili peppers is crucial for achieving automated operations. In complex field environments, challenges such as overlapping plants, branch occlusions, and uneven lighting make detection difficult. This study conducted comparative experiments to select the optimal detection model based on YOLOv8 and further enhanced it. The model was optimized by incorporating BiFPN, LSKNet, and FasterNet modules, followed by the addition of attention and lightweight modules such as EMBC, EMSCP, DAttention, MSBlock, and Faster. Adjustments to CIoU, Inner CIoU, Inner GIoU, and inner_mpdiou loss functions and scaling factors further improved overall performance. After optimization, the YOLOv8 model achieved precision, recall, and mAP scores of 79.0%, 75.3%, and 83.2%, respectively, representing increases of 1.1, 4.3, and 1.6 percentage points over the base model. Additionally, GFLOPs were reduced by 13.6%, the model size decreased to 66.7% of the base model, and the FPS reached 301.4. This resulted in accurate and rapid detection of chili peppers in complex field environments, providing data support and experimental references for the development of intelligent picking equipment.

In-field grading and sorting technology of apples: A state-of-the-art review

Apple is one of the most popular fruits. In-field grading and sorting of apples would enhance growers’ economic benefits by lowering production costs. This study reviews the key components and progress of the quality inspection algorithm for in-field grading and sorting of apples. Four key components (e.g., conveyor, imaging chamber, sorting actuator, and bin filler) are presented in detail, followed by summarizing the shortcomings of these components. The apple’s external (color, size, and defects) and internal quality inspection technologies, such as optical technologies of visible light, near-infrared (NIR), hyperspectral/multispectral imaging (HSI/MSI), and structured illumination (SI) were presented. Despite the excellent detection performance of emerging technologies (e.g., HSI, MSI, and SI), visible light is still dominantly used for in-filed grading. Challenges in getting information on the whole surface area of the apple, uneven lighting, machine size, throughput, and associated costs hamper the commercialization of apple in-field grading and sorting equipment. At present, more efforts should be devoted to internal quality inspection, by developing reliable, fast, and accurate detection equipment and algorithms. With the advancement of sensors and automation algorithms, as well as the emergence of mechanical systems that are suitable for in-field use, it is anticipated that the apple in-field grading and sorting equipment that inspects both external and internal quality will be realized and commercialized in the near future.